Self-esteem Stability and Change at Home versus at Work: An Application of the Latent State-Trait Model for the Combination of Random and Fixed Situations (LST-RF)

ABSTRACT

Recent research has shown that even stable psychological constructs, like self-esteem, are not static entities. Accordingly, research on state-trait decomposition is growing, but few studies have been conducted on investigating potential differences across situations. In this contribution, we used a Latent State-Trait model for the combination of Random and Fixed situations (LST-RF) to investigate self-esteem state-trait decomposition at home versus at work, in order to examine the impact of the organizational environment on some characteristics of self-esteem stability and change. Workers from various sectors (N = 161) completed a battery at T0 (age, gender, and organizational membership) and then responded to a questionnaire investigating their degree of global self-esteem and self-concept clarity across two days in 8 random situations that were nested within 2 fixed situations (home-work). Results showed that, contrary to our hypothesis, there were no substantial differences in self-esteem state-trait decomposition at home versus at work, while there was a significant mean-level decline of self-esteem when at work (consistent with the Conservation of Resources Theory) which was positively counteracted by organizational membership (according to the Sociometer Theory). In conclusion, we demonstrated that considering fixed situations may advance our knowledge on self-esteem state-trait decomposition.

KEYWORDS:

• self-esteem
• self-concept
• Latent State-Trait models
• random and fixed situations
• LST-RF

Acknowledgments

The authors thank Christian Geiser for his help in (a) computing coefficients in both the Random and the Fixed Situation Part of the LST-RF Model, (b) reviewing some parts of “The Role of Situations in LST Models” paragraph.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Mplus syntax for model in Figure 1 is reported in Supplementary Material

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15283488.2022.2115495

Notes

1. We point out that “uniqueness” (also called “indicator-specific” effect) is considered a source of method effect. For example, see, Geiser and Lockhart (2012) and Geiser et al. (2019, p. 80, $1.2.1). Here we separated uniqueness from classical method effects in order to give a broader overview of the sources of variance in LST models, and because uniqueness has an important role in some LST models (e.g., Alessandri et al., 2013; Tisak & Tisak, 2000).

2. A fundamental difference to note is that the U_t in LST-R is replaced with U_f in LST-RF.

3. This finding would suggest that self-esteem state(trait) variance was higher(lower) than that found in general population samples.

4. That is, even if the correlation between trait self-esteem and trait organizational variables were all high (> .75), the correlation between state self-esteem and state in-role/extra-role performance was .613, while the correlations with state work engagement and state affective commitment were .398 and .294, respectively. This finding would suggest that – regardless the size of the trait-level correlation – the state-level correlation may have a different size.

5. We point out that Geiser, Litson et al. (2015) proposed various versions of the LST-RF approach. Here, we used one of those versions, that is the MTMS model with long data (i.e., multilevel or hierarchical data). However, researchers should decide which version to use based on the characteristics of the data and their hypotheses (e.g., using a wide-data approach when time points are few, or using a Singletrait-Multistate [STMS] model when observed indicators are highly correlated and thus either refer to a single trait).

Additional information

Funding

Enrico Perinelli was supported by a research fellowship [WeBeWo LAB n. 40600134] granted by the University of Trento